In this note, we study a simulation optimization problem of selecting the alternative with the best performance from a finite set, or a so-called ranking and selection problem, in a special low-confidence scenario. The most popular sampling allocation procedures in ranking and selection do not perform well in this scenario, because they all ignore certain induced correlations that significantly affect the probability of correct selection in this scenario. We propose a gradient-based myopic allocation policy that takes the induced correlations into account, reflecting a tradeoff between the induced correlation and the two factors (mean-variance) found in the optimal computing budget allocation formula. Numerical experiments substantiate the efficiency of the new procedure in the low-confidence scenario.

中文翻译：

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基于梯度的近视分配策略：低置信度场景下的高效采样过程


在本文中，我们研究了在特殊的低置信度场景中从有限集合中选择性能最佳的替代方案的模拟优化问题，或所谓的排序和选择问题。排序和选择中最流行的抽样分配过程在这种情况下表现不佳，因为它们都忽略了某些诱发的相关性，而这些相关性会显着影响这种情况下正确选择的概率。我们提出了一种基于梯度的短视分配策略，该策略考虑了诱发相关性，反映了诱发相关性与最优计算预算分配公式中发现的两个因素（均值-方差）之间的权衡。数值实验证实了新程序在低置信度场景下的效率。